A: Super-resolution is a technique to enhance the resolution of an imaging system. In this FAQ we will refer to the particular type of super-resolution which can improve resolution of digital imaging systems beyond their sensor and optics limits.

Q: So, is it real?

A:
It looks like a science fiction, but there are solid physical concepts behind the process. To be sure, there are limits to what you can achieve with super-resolution processing, which depends on numerous factors (see ""What levels of increased resolution are realistic?" for an in-depth discussion on limits).

Q: Why does it work?

A:
For a concise answer on all types of super-resolution, please consult Wikipedia. There you will find a deeper explanation for any particular case of multi-image digital super-resolution:
There are two key components in every digital imaging system: the sensor and the lens. There are two different types of image degradation introduced by these two components individually:

Optical blur.

Limit on the highest spatial frequency the given sensor can record.

Optical blur is simply a reduction in amplitude of high spatial-frequency components of the image. It should have been possible to reconstruct a perfect, high-resolution image after optical blur by applying an inverse sharpening. Unfortunately, this is followed by degradations cause by the sensor and simple sharpening is not going to work.
The key to super-resolution is the presence of so-called aliased components in the sensor output. These are present due to the fact that the sensor is constructed from a finite number of discrete pixels. These are higher spatial-frequency components than the sensor can handle that should not normally be present in the sensor output. Fortunately, due to imperfect anti-aliasing filters in the imaging system (or the complete lack of them) and due to lower than 100% fill-factor (the percentage of the area that is sensitive to light in each sensor pixel) the aliased components remain in the image. Even the best anti-aliasing filter can only lower these components by some amount but cannot eliminate them completely. Aliased components are typically unwanted in the normal image since they might manifest themselves in a form of moire effect or other unwanted artefacts.
Another, photography-specific reason why super-resolution works is that real sensors are composed of Color Filter Arrays (CFAs). CFA can record only a single color at each pixel location. This lowers the upper spatial frequency that can be recorded by the sensor even more. But having multiple, slightly shifted images makes it possible to reconstruct full color at each pixel site.

Q: Aliasing components? Do they really exist?

A:
This is a long one. Let us model an "ideal" camera - with ideal lens (no blur, no distortions) and a sensor completely covered by an array of pixels. Every pixel registers a signal proportional to the amount of light it received.

How would such camera image a target of black-and-white lines, if the width of a line were exactly the same as the dimension of a pixel. The image will de quite different in case all the lines fall exactly to the pixels and in case the lines fall between the pixels:

Luckily, real scenes usually do not have exactly the same structure as the sensor has. To make our model more realistic, we will tilt the lines - so if in some part of the picture the edges of the lines match the edges of the pixels in the sensor, they will not match in other parts. This is how the tilted lines will be imaged by our ideal camera:

The contrast between black and white lines differs from 100% of the original contrast to none. Looks strange already, doesn't it?

What happens if we try to image line pairs of higher frequency? See the pictures below: the lines are visible, but they have different directions, and, moreover, thicker width - that is, lower frequency than in the original!

This is caused by so-called aliasing. The sensor, which is not able to image a pattern of frequency higher than 0.5 cycles/pixel, delivers not only lower contrast but completely wrong pictures. If the scene being imaged has a regular pattern, the artifacts are known as Moiré pattern.

Digital cameras usually have anti-aliasing filters in front of the sensors. Such filters prevent the appearance of aliasing artifacts, simply blurring high-frequency patterns. With the ideal anti-aliasing filter, the patterns shown above would have been imaged as a completely uniform grey field. Fortunately for us, no ideal anti-aliasing filter exists and in a real camera the aliased components are just attenuated to some degree.

Q: How does it work

A: The first step is to accurately align individual low-resolution images with sub-pixel precision.

After the images are aligned, a number of techniques are possible, both iterative and non-iterative, complex or simple, slow or fast. What is common in all of the techniques is that information encapsulated in the aliased components is used to recover spatial frequencies beyond sensor resolution and a de-blurring is used to reverse degradation caused by the optical system.

Of course, the real reconstruction process is much more complex due to the presence of at least the following phenomena:

Sensor noise. The noise itself degrades the image quality, but most importantly it reduces the ability to recover and separate aliased components that are low in amplitude and typically buried under noise floor.

Uncertainty in real registration offsets of individual images. Since the precise camera position and orientation in space is not known during super-resolution processing, it has to be estimated from the low resolution scenes themselves, which introduces errors.

ú Diffraction limit. It is said that the optical system has fundamental limits on resolution where two close subjects cannot be resolved one from another. There are methods that allow breaking this limit as well under certain assumptions (see Wikipedia).

Q: What levels of increased resolution are realistic?

A: It is highly variable depending on the optical system exposure conditions and what post-processing is applied. As a rule of thumb, you can expect and increase of 2x effective resolution from a real-life average system (see MTF measurements) using our methods. We've seen up to a 4x increases in some cases. You can get even higher results under controlled laboratory conditions, but that's only of theoretical interest.

Q: What kind of source material is suitable for super-resolution processing?

A: Here are some rules:

The less post-processing, the better. Avoid sharpening, for example.

Heavy compression is particularly bad. It will destroy all the aliasing components.

Heavy compressed video that relies on inter-frame prediction is also very bad. The only positive outcome from applying super-resolution to heavily compressed materal is that it will decrease artifacts from compression itself, and might suppress noise, but don't hope for a real increase in resolution.

16 bit images are better than 8bit because they preserve low-amplitude effects.

RAW images are good for super-resolution, not only because they lack any post-processing but also because they give information about the exact layout of the Color Filter Array.

Blur in the images is no good.

Sensor noise is also no good, but surprisingly it has significantly less affect than blur. So, if you have to choose: blur from an unsteady hand or noise from high ISO - choose noise.

The optimal count of images to achieve reasonable super-resolution is 8. Do not expect any improvements with more than 16 input images.

Q: What kinds of resolution processing are available today?

A: There are two major classes of super-resolution:

reconstruction-based super-resolution

recognition-based super-resolution

Recognition-based super-resolution is trying to detect or identify certain pre-configured patterns in the low resolution data. It has a limited application area (e.g. forensic face-detection).

It can be dependent or independent of a particular imaging system. The image-system-dependent method has the advantage of taking into account all the characteristics of a particular system and thus producing better results.

Super-resolution methods can also be divided by source/output type:

Single-image - in this case we're talking about deblurring, and there is no real resolution increase.

Multiple still images in, single image out - used in photography

Video-sequence super-resolution - a wide variety of methods were recently brought into existence due to the growing popularity of HDTV. Most of them are not based on real super-resolution methods and are as simple as edge enhancement.

A paper from Microsoft Research that attempts to estimate the practical limits of super-resolution. The scope of this paper is limited to a particular subclass of linear-only, reconstruction-based super-resolution algorithms. In any case, the obtained bounds do correlate well with the practical results (top limit is ~5x under ideal conditions, ~2x in real life).

Another property that can be considered a weakness in applications where the imaging system is unknown is that to obtain optimal performance the algorithm is tuned for a particular imaging system (individual profiles are used for each sensor/lens combination). See this example though.